Maternal mortality prediction using WHO near miss criteria and Maternal severity models: Evidence from a Tertiary Care Study in India

Maternal mortality prediction using WHO near miss criteria and Maternal severity models: Evidence from a Tertiary Care Study in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Maternal mortality prediction using WHO near miss criteria and Maternal severity models: Evidence from a Tertiary Care Study in India Bharathi Ramu¹, Anil P Hogade², Hema S Patil, Sanjeev Chougale⁴ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6596205/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Maternal mortality remains a critical public health issue, particularly in low- and middle-income countries (LMICS). Over the past two decades, there has been a significant global reduction in maternal mortality. As maternal deaths continue to decline, measuring maternal morbidity—including the near-miss ratio, mortality index, severe maternal outcome ratio, and maternal near-miss mortality ratio—is essential for assessing the quality of obstetric care. This study was conducted to validate the performance of the WHO MNM criteria and compare predictive models that integrate the Maternal Severity Score and Maternal Severity Index to predict maternal mortality in a tertiary care setting. Methods A prospective observational study was conducted at KAHER’s Dr. Prabhakar Kore Hospital and Medical Research Centre, Belagavi, India (1st February 2024–31st January 2025). Pregnant women fulfilling the WHO maternal near-miss criteria were enrolled, and diagnostic accuracy tests for overall WHO criteria and organ dysfunction severity markers were performed. Pearson’s correlation coefficient was used to determine the association between MSS and MSI. Two binary logistic regression models to predict the probability of maternal death were developed and compared via the area under the receiver operating curve (AUROC), with additional assessment via Nagelkerke R² and the Hosmer–Lemeshow goodness-of-fit test. Results Out of the 295 women identified with the WHO maternal near miss criteria, 191 fulfilled the criteria for maternal near misses, and 15 resulted in maternal death. The severe maternal outcome ratio (SMOR) was 51.7, the MNM ratio was 47.9 per 1,000 live births, and the mortality index was 7.27%. indicating that a majority of women with life-threatening conditions survived with better quality of care. The diagnostic accuracy of the WHO near-miss criteria showed good sensitivity (100%) and high specificity (93.11%) and improved significantly, up to 95.43%, when the organ dysfunction subset was used. The number of cases with severity markers per thousand deliveries ranged from 0.49–24.82. The correlation between the Maternal Severity Score and the Maternal Severity Index was strong (R = 0.805, p < 0.001). supporting the internal validity of severity assessment tools, the multivariate logistic regression model that included additional clinical parameters performed better, achieving an AUROC of 0.939, indicating excellent discriminatory ability for predicting maternal mortality and underscoring the clinical utility of the enhanced model. Maternal near miss Maternal severity score Maternal severity index. Mortality Index Prediction Quality of care. Figures Figure 1 Figure 2 Figure 3 Introduction Women's health during pregnancy, delivery, and the postpartum phase is the focus of maternal health, and a marker of the efficiency of the health care system of any country [ 1 ] In any setting, women who develop severe acute morbidity during pregnancy share many pathological and circumstantial factors related to their condition. While some of these women die, a proportion of them narrowly escape death. Maternal death is defined as the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and the site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management, but not from accidental or incidental causes[ 2 ]. Nearly 1,000 women die every day, and ten million women present with complications related to pregnancy every year around the world.[ 3 ] To evaluate maternal health, women who survive life-threatening complications must be thoroughly examined. In this context, the WHO defines maternal near miss (MNM) as "a woman who almost died but survived a complication that occurred during pregnancy, childbirth, or within 42 days of termination of pregnancy" [ 2 ] [ 4 ]. To reduce avoidable maternal mortality and enhance maternal health outcomes, addressing maternal near-miss morbidity is imperative. The World Health Organization (WHO) adopted maternal near-miss definitions and identification criteria in 2008. These criteria were developed via a comprehensive evaluation of earlier research. critical evaluations were conducted on intensive-care prognostic scoring systems, including the APACHE II score, SAPS, MODS, and especially the SOFA score as a possible indicators of clinical severity [ 5 ] [ 6 ]. Physiological changes during pregnancy affect some of the markers and may lead to overestimation or underestimation of severity depending on parameters, limiting the use of the scoring system in obstetric patients, and complications such as eclampsia, acute fatty liver during pregnancy, HELLP, and amniotic fluid embolism have distinct characteristics that may not be adequately addressed by the scoring system [ 7 ] [ 8 ]. The WHO's near-miss approach is a standardised measure consisting of 25 severity markers of life-threatening conditions, and each severity marker is associated with a specific mortality risk. The maternal severity score (MSS) assigns a score based on the presence of specific severity markers (life-threatening conditions), and the maternal severity index (MSI) estimates the probability of death of women with pregnancy-related complications, particularly those with life-threatening conditions; both of these tools are very useful in assessing the quality of maternal health care [ 9 ] [ 10 ]. Globally, between 2000 and 2020, the global maternal mortality rate (MMR) declined by 34.3%, from 339 deaths to 223 deaths per 100,000 live births[ 11 ] [ 12 ]. As per the 2020 report by the United Nations Maternal Mortality Estimation Inter-Agency, India’s maternal mortality ratio (MMR) has experienced a notable reduction, decreasing from 384 per 100,000 live births in 2000 to 103 in 2020. In contrast, the global MMR experienced a decline to only 223 from 339 over the same timeframe. The annual reduction rate (ARR) for the global MMR between 2000 and 2020 was 2.07%, whereas India achieved a more significant decline, with an ARR of 6.36%, highlighting the advancements made in the maternal health initiative [ 13 ] [ 14 ]. The Government of India (GOI) guidelines mandate that maternal death reviews (MDRSs) be systematically carried out in all healthcare facilities. Nonetheless, reviews of maternal near miss (MNM) events offer additional benefits over MD reviews [ 15 ] [ 16 ]. In the last ten years, various studies on MNM [ 17 ],[ 18 ],[ 19 ], have been undertaken in India and other developing countries, [ 20 ],[ 21 ], utilising the WHO criteria. The results emphasise the importance of MNM reviews in enhancing maternal health policies, improving the quality of healthcare delivery, and averting maternal deaths through timely interventions [ 22 ].Hence, this study was conducted to assess the WHO MNM criteria and Maternal severity markers as predictors of maternal mortality Methods Study Design : A prospective observational study was conducted at KAHER’s Dr. Prabhakar Kore Hospital and Medical Research Centre, Belagavi, India, for 1 year (1 February 2024–31 January 2025) Study Setting : A hospital offering 2000+ beds and state-of-the-art infrastructure, including 270 ICU beds and a wide range of specialities. Annually, 3500 to 4000 deliveries are conducted at the facility. The hospital acts as a provincial referral hospital for high-risk obstetric cases from health centres and other neighboring states of Goa and Maharashtra. Ethical Consideration : Ethical clearance was obtained from the JNMC Institutional Ethics Committee KAHER/EC/23-24/349-1) (CTRI Registration number: CTRI/2024/03/063871) approved on Feb 1, 2024, confirmed registration on March 8, 2024. Informed consent was obtained from pregnant women who were admitted and included in the study. The WHO near-miss tool was used for data collection. The confidentiality of the data was maintained Eligibility criteria: Study population : Pregnant women fulfilling the WHO maternal near miss criteria (2011) and having maternal deaths as defined by the WHO were included in the study. The inclusion criteria were the presence of any of the conditions listed in the WHO maternal near miss criteria (i.e., potentially life-threatening conditions, WHO life-threatening conditions) and maternal death. The exclusion criteria were as follows : Women who developed complications more than 42 days after the termination of pregnancy were not eligible. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines Operational definitions: Maternal near-miss (MNM) refers to a woman who nearly died but survived a complication that occurred during pregnancy, childbirth or within 42 days of termination of pregnancy. Maternal death (MD) is the death of a woman while pregnant or within 42 days of termination of pregnancy or its management, but not from accidental or incidental causes. Severe maternal outcome ratio (SMOR) refers to the number of women with life-threatening conditions (MNM + MD) per 1000 live births (LB). This indicator gives an estimate of the amount of care and resources that would be needed in an area or facility [SMOR = (MNM + MD)/LB]. MNM ratio (MNMR) refers to the number of maternal near-miss cases per 1000 live births (MNMR = MNM/LB), This indicator gives an estimation of the amount of care and resources that would be needed in an area or facility. Maternal near-miss mortality ratio (MNM: 1 MD) refers to the ratio between maternal near-miss cases and maternal deaths. Higher ratios indicate better care. Mortality index refers to the number of maternal deaths divided by the number of women with life-threatening conditions expressed as a percentage [MI = MD/ (MNM + MD)]. The higher the index the more women with life-threatening conditions die (low quality of care), whereas the lower the index the fewer women with life-threatening conditions die (better quality of care). Statistical analysis All the statistical analyses were performed via IBM SPSS version 24, R software version 4.3.3, and Excel. The performance of the WHO criteria for case identification was assessed in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), and negative likelihood ratio (NLR). Sensitivity and specificity were calculated to determine the accuracy of maternal death (MD) and near-miss identification. Likelihood ratios were used to assess the strength of case identification, and predictive values were used to quantify the probability of a correct classification. The associations between maternal severity markers and mortality risk were analyzed via relative risk (RR) with 95% confidence intervals (CIs) for clinical indicators. The chi-square test was employed to determine the statistical significance of associations between categorical variables, with the significance level set at p<0.05. Descriptive statistics, including the mean and standard deviation, were used to summarize the maternal severity score and maternal severity index. A two-tailed significance test was conducted to determine whether the correlation was statistically significant, with the significance level set at p<0.01. The association between the maternal severity score and maternal mortality was analyzed via Pearson’s correlation coefficient to determine the strength and direction of the relationship. A significance test was conducted at the 0.01 level to assess the statistical significance of the correlation. To compare the performance of Models I and II and the maternal severity score in predicting maternal death, binary logistic regression was applied. The Hosmer–Lemeshow test was used to assess the goodness of fit, whereas the Nagelkerke R² test was employed to evaluate the explanatory power of the models. The percentage of maternal deaths with a model-estimated death probability greater than 50% was analyzed for different subpopulations. Finally, the area under the operating characteristic curve (AUROC) was calculated to measure the models' discrimination ability in predicting maternal deaths. The statistical analyses were validated against existing evidence to determine the robustness and reliability of the findings in maternal health research. Results A total of 4068 women were admitted to the facility during the one-year data collection period, and live born babies were 3978. The study population comprised 310 women meeting the inclusion criteria. Among this population, 295 women met any of the WHO criteria, women presenting with at least one of the severity markers classified as WHO life-threatening conditions included 15 maternal deaths and 191 maternal near misses, representing a severe maternal outcome ratio of 51.7 per 1000 live births. The MNM:MD ratio was 12.73:1, and the MNM ratio was 47.9 per 1000 live births, with a mortality index of 7.28%. These findings reflect the burden of severe morbidity in the study setting. (Table 1) shows the diagnostic performance of the WHO near-miss criteria in two populations: women with overall WHO-defined conditions and those with WHO-defined organ dysfunction. The criteria achieved a sensitivity of 100% (95% CI: 78.20–100.00) and a specificity of 93.11% (95% CI: 92.31–93.85). When the analysis focused on organ dysfunction markers, the diagnostic accuracy improved to 95.43% (95% CI: 94.75–96.04), with a higher positive likelihood ratio and specificity, while maintaining optimal sensitivity and negative predictive value. (Table 2) presents the associations of severity markers (WHO life-threatening conditions) with mortality and the prevalence of cases with severity markers per thousand deliveries, which range from 0.49-24.82. The specific mortality rate varied from 6.39% to 100%, and all the life-threatening conditions were heterogeneously associated with maternal deaths. 7 Life-threatening conditions present relative risks of 5 and 10, Gasping (RR: 12.63), Oxygen Saturation 6 mg/dL) (RR: 7.77), Severe Tachypnoea -RR >40/min (RR: 5.47) Severe Thrombocytopenia 3.5 mg/dL (RR: 5.18) these markers are significant indicators of deteriorating physiological conditions. Three markers (Use of Vasoactive Drugs (RR: 18.23), Oliguria unresponsive to fluids and diuretics (RR: 14.59), Severe Hypoxemia -PaO₂/FiO₂ < 200 (RR: 10.78) presented with relative risks ranging from 10 to 20, suggesting circulatory collapse and severe metabolic disorders, and 3 conditions i.e., Shock (RR: 35.72), Severe Hypoperfusion (Lactate >5 mmol/L or >45 mg/dL) ( RR: 43.09), Severe Acidosis (pH 60.i.e CPR (RR: 155.65), Intubation & Ventilation (non-anaesthetic) (RR: 157.73) Shock (RR: 35.72; p < 0.001), vasoactive administration (RR: 40.74; p < 0.001), cardiac arrest (RR: 14.5; p < 0.001), and CPR (RR: 155.65; p < 0.001). severe hypoxemia (RR: 10.78; p < 0.001), oxygen desaturation (RR: 8.16; p < 0.001), and non-anesthesia intubation (RR: 157.73; p < 0.001) were also strong predictors. Metabolic dysfunction as severe hypoperfusion (RR: 43.09; p < 0.001), acidosis (RR: 25.38; p < 0.001), and renal dysfunction (e.g., oliguria: RR: 14.59; p < 0.001), showed a significant link to mortality rates. In contrast, neurological disorders (including prolonged unconsciousness, stroke, and status epilepticus) and uterine complications (e.g., infection-related hysterectomy) did not correlate with mortality (p > 0.05) [23]. Additionally, massive transfusion was linked to a lower risk (RR: 1.99; p = 0.009), suggests efficient management of complications. These findings indicate extremely high mortality risks and also identify severity markers as significant predictors of physiological deterioration and adverse outcomes. (Table 3) shows that the maternal severity score has a mean of 1.56 with a standard deviation of 2.08, indicating that most women were managed before complications became life-threatening, suggesting that early intervention effectively reduced the risk of maternal mortality. The Maternal Severity Index has a higher mean of 3.77, with a much larger standard deviation of 12.95, showing its strong predictive value for identifying women at increased risk of maternal mortality. The correlation between the Maternal Severity Score and the Maternal Severity Index is strong (R = 0.805) and statistically significant (p = 0.0001*), suggesting that higher scores on one measure are associated with higher scores on the other. This significant positive correlation indicates that both measures are closely related in assessing maternal health severity. (Figure 1) A scatter plot illustrates a positive correlation between the Maternal Severity Score and the Maternal Severity Index, with a clear upward trend. As the severity score increases, the severity index also tends to rise, confirming the significant relationship between the two variables. (Table 4) illustrates the relationships between maternal severity scores and mortality across different severity score categories. The sharp increase in mortality with higher severity scores suggests a strong association between the severity of a maternal condition and the likelihood of death. The Pearson correlation coefficient of 0.383 is significant at the 0.01 level. supports the use of the maternal severity score as a predictor of maternal mortality risk, suggesting that higher scores reflect more critical conditions that require immediate medical attention. (Table 5) shows the performance of two binary logistic regression models (Model I and Model II) and the maternal severity score in predicting maternal death. Model I: This model, which is based solely on the maternal severity score, yielded an AUROC of 0.775 (95% CI: 0.725–0.825) (Figure 2) reflecting Hosmer–Lemeshow test produced a p-value of 0.012, showing a marginal fit, whereas the Nagelkerke R² was 0.405, suggesting that approximately 40.5% of the variance in maternal mortality could be explained by this model. Model II: The multivariate model that included additional clinical parameters performed markedly better, achieving an AUROC of 0.939 (95% CI: 0.911–0.966) (Figure 3). The Hosmer–Lemeshow test p value for Model II was 0.361, indicating a very good fit, and the Nagelkerke R² value was 0.789, reflecting robust explanatory power for predicting maternal deaths. Furthermore, the percentage of maternal deaths with a model-estimated death probability greater than 50% was greater in Model II (60.3%–62.8%) than in Model I (58.7%–60.6%), reinforcing the clinical utility of including both the MSS and clinical parameters in the prediction of severe maternal outcome. Table 1 Diagnostic accuracy of the WHO criteria with severity markers ( life- threatening complications) in the prediction of maternal death . All women Organ dysfunction Maternal death Maternal death + - + - WHO criterion + 15 295 15 191 - 0 3978 0 3978 Accuracy estimator Sensitivity (95% CI) 100 (78.20-100.00) 100 (78.20 - 100.00) Specificity (95% CI) 93.11 (92.31- 93.85) 95.43 (94.75 - 96.04) Positive Likelihood Ratio (95% CI) 14.52 (13.00 -16.20) 21.87 (19.04 - 25.12) Negative Likelihood Ratio (95% CI) 0 0 Positive Predictive Value (95% CI) 53.7 (50.96- 56.43) 52.39 (48.93 - 55.83) Negative Predictive Value (95% CI) 100 (99.91- 100.00) 100 (99.91- 100.00) Table 2 Association of severity markers (WHO-life-threatening conditions) with maternal deaths . MD (Maternal death) Cases presenting the severity marker per 1000 deliveries* Mortality Relative Risk(95%CI) p value Cardiovascular dysfunction + - Shock + 14 44 14.25 24.13 35 .72 (4.57 - 279.33) <0.001* - 1 147 Use Of Vasoactive + 11 16 6.63 40.74 18.23 (5.2 - 63.87) <0.001* - 4 175 Cardiac arrest + 3 0 0.73 100 14.5 (0.69 - 306.25) <0.001* - 12 191 CPR + 14 3 4.17 82.35 155.65 (15.18 - 1595.64) 5 mmol or >45 mg/dl + 13 14 6.63 48.15 43.09 (8.83 - 210.27) <0.001* - 2 177 severe acidosis PH <7.0 + 13 29 10.32 30.95 25.38 (5.44 - 118.43) <0.001* - 2 162 Respiratory dysfunction Gasping + 3 1 0.98 75 12.63 (1.22 - 130.7) 40 bpm) + 3 6 2.21 33.33 5.47 (1.22 - 24.62) <0.001* - 12 185 Severe hypoxemia (PA02/FiO2 <200 + 6 6 2.94 50 10.78 (2.89 - 40.13) <0.001* - 9 185 O2 Saturation <90% for 60 min + 9 23 7.86 28.13 8.16 (2.66 - 25.03) <0.001* - 6 168 Intubation & Ventilation not related to anesthesia + 15 18 8.11 45.45 157.73 (9.04 - 2751.91) <0.001* - 0 173 Renal dysfunction Oliguria Nonresponsive to fluids and Diuretics + 6 3 2.21 66.67 14.59 (3.13 - 67.99) <0.001* - 9 188 Dialysis for acute renal failure + 2 3 1.22 40 6.18 (0.95 - 40.35) 3.5 mg/dl) + 1 2 0.73 33.33 5.18 (0.44 - 60.95) 0.016* - 13 189 Coagulation/hematological dysfunction Failure to form clots + 8 14 5.40 36.36 9.56 (3.02 - 30.22) 5 units + 7 56 15.48 11.11 1.99 (0.69 - 5.74) 0.009* - 8 135 Severe acute thrombocytopenia (<50.000 platelets) + 5 21 6.39 19.23 3.46 (1.08 - 11.1) 6.0 mg/dl + 2 2 0.98 50 7.77 (1.01 - 59.69) 12 hrs. + 0 5 1.22 0 1.22 (0.06 - 23.38) 0.611 - 15 186 Stroke + 0 2 0.49 0 2.72 (0.12 - 63.05) 0.749 - 15 189 Status epilepticus + 0 10 2.45 0 0.62 (0.03 - 11.18) 0.469 - 15 181 Uterine dysfunction hemorrhage + 7 94 24.82 6.39 0.91 (0.32 - 2.61) 0.245 - 8 97 Infection Leading to Hysterectomy + 0 3 0.73 0 1.93 (0.09 - 40.41) 0.695 - 15 188 Hysterectomy (prevention of PPH) + 0 9 2.21 0 0.69 (0.04 - 12.51) 0.469 - 15 182 'N=3987 (Number of deliveries) p < 0.05* Table 3 Correlation between maternal severity scores and the maternal severity index according to the Karl Pearson correlation coefficient Variables Mean Std. Dv. r (X, Y) t value p value Maternal severity scores 1.57 2.08 Maternal severity Index 3.77 12.95 0.8052 23.75 0.0001* *Correlation is significant at the 0.01 level (2-tailed). Table 4 Association of the maternal severity score with maternal death Maternal severity score (Counts) Total Mortality RR 95% CI p value 0 104 0 0.06 0.00-1.05 0.0543 1 102 0 0.07 0.00-1.08 0.0569 2 46 0 0.18 0.01-2.98 0.2327 3 21 0 0.43 0.02-6.87 0.547 4 13 0 0.69 0.04-10.90 0.7898 5 8 3 9.44 3.29-27.03 0.0001* 6 4 0 1.98 0.13-28.71 0.6164 7 3 3 25.58 14.69-44.54 0.0001* 8 3 3 25.58 14.69-44.54 0.0001* 9 2 2 23.69 13.91-40.33 0.0001* 10 1 1 22.07 13.22-36.82 0.0001* 11 1 1 22.07 13.22-36.82 0.0001* 13 2 2 23.69 13.91-40.33 0.0001* The Pearson correlation coefficient between the maternal severity score and mortality is 0.383 Table 5 Performance Comparison of Models I and II and Maternal Severity Score in Predicting Maternal Death Hosmer‒Lemeshow test Nagelkerke R 2 test Percentage of maternal deaths with a model-estimated death probability >50% (subpopulation "A") Percentage of maternal deaths with a model-estimated death probability >50% (subpopulation "B") AUROC with 95% CI Model I 0.012 0.405 58.70% 60.60% 0.775 (0.725 – 0.825) Model II 0.361 0.789 60.30% 62.80% 0.939 (0.911 – 0.966) Maternal Severity Score N.A. N.A. N.A. N.A. 0.875 (0.816 – 0.934) Discussion This study assessed the effectiveness of the WHO maternal near miss (MNM) criteria and maternal severity markers that predict maternal mortality in a tertiary care setting. These findings show that the WHO criteria can reliably detect maternal deaths because of their high sensitivity and diagnostic accuracy and strong association of Severity markers with maternal mortality. In our study, the Severe Maternal Outcome Ratio (SMOR) of 51.7 per 1000 live births, indicating an increased burden of life-threatening maternal complications. These Observations align with existing findings from India and other low- and middle-income countries. To illustrate, the WHO multicountry Survey on Maternal and Newborn Health reported an SMOR of 40.3 per 1000 live births across facilities in Africa, Asia, Latin America, and the Middle East, including India [ 24 ]. However, single-country studies showed higher SMOR: Rwanda [ 25 ], Tanzania [ 20 ], with SMOR of 81.1, 101.1, respectively. Purandare et al.'s multicenter study revealed 92.3 per 1000 live births [ 26 ], whereas Roopa et al.'s 76.2 in Karnataka [ 27 ], Maharashtra's 131.5 [ 28 ], These high SMOR values, especially in referral centers, emphasize the cumulative effects of delayed presentation, inadequate antenatal surveillance, and the referral of high-risk cases at a critical stage of complications. Our study showed a Mortality Index (MI) of 7.27%, which is lower compared with earlier studies, despite the increased SMOR. In particular, 13.1% MI was reported by Souza et al. [ 24 ]and 12% by Verschueren et al [ 29 ], 17.7% MI showed by Nelissen et al. [ 20 ], and 11.2% MI was reported by Purandare et al. [ 26 ], and we observed MNM:MD ratio 12.7:1 which is in line with Kulkarni et al [ 30 ] and lower compared to Goldenberg et al which is 26:1 [ 31 ]. Even when a high SMOR is present, a relatively lower MI suggests that even though many women suffered from severe morbidity, the healthcare system was able to prevent a significant number of maternal deaths by ensuring that they had access to timely interventions and critical care. Furthermore, the Maternal Near Miss Ratios (MNMR), 47.9 per 1000 live births, highlight the need to improve health system responsiveness. These findings align with findings from various Indian and international studies. For example, 16.6/1000 live births by Verma et al. were conducted in Uttar Pradesh, India, which is lower than our study. Purandare et al. noted an MNMR of 88.3 per 1000 live births in a multicentric Indian study [ 26 ], Roopa et al. recorded 80.2 per 1000 live births in Karnataka [ 27 ],Rathod et al. documented 121.4 per 1000 live births in Maharashtra [ 28 ], These high MNMR highlight the major challenge presented by life-threatening maternal conditions treated at tertiary centers. The findings of our study regarding diagnostic accuracy align closely with those reported by Souza et al. (2012), which analyzed data from 29 countries in the WHO Global Survey on Maternal and Perinatal Health. Their multicentric study achieved a negative predictive value (NPV) of 99.9% and nearly 100% sensitivity, demonstrating that the WHO criteria effectively identify all maternal deaths and near-miss events without missing serious complications [ 32 ].T. Witteveen et al.study [ 33 ] showed opposite findings that organ dysfunction-based criteria of the WHO MNM tool fail to identify nearly two-thirds of SAMM cases and more than one-third of maternal deaths [ 33 ].Tura et al. studied sub-Saharan Africa and verified the WHO MNM criteria, finding specificity range of 85–96% and a sensitivity of 100%. This aligns with our findings, especially regarding specificity in our resource setting [ 34 ] [ 35 ]. Our study's modest PPV (~ 53%) is consistent with the varying PPV values across situations reported by Souza et al. and Tura et al. This difference may be explained by the case mix, resource availability, and the application or interpretation of specific management-based criteria, which may exaggerate severe morbidity in institutions with better access to advanced care [ 36 ]. A significantly greater relative risk (RR) for maternal death was shown by several severity markers, particularly those linked to metabolic, respiratory, and cardiovascular failure. Conditions such as cardiac arrest, the need for cardiopulmonary resuscitation, shock, and severe lactic acidosis have RRs over 40, indicating rapid systemic decompensation and critical illness. These results are consistent with those of multicountry WHO research, which revealed a consistent association between increased mortality risk and shock and organ failure [ 37 ]. Cardiovascular severity markers such as shock, use of vasoactive drugs, cardiac arrest, and CPR demonstrated strong associations with mortality (p < 0.001), in line with findings from WHO’s Multicounty Survey, circulatory dysfunction as a key predictor of maternal mortality. Severe hypoxemia and non-anaesthesia intubation, as a key contributor and predictor of maternal death, comply with previous validation studies [ 38 ] [ 39 ]. Severe hypoperfusion, acidosis, and oliguria are notably associated with poor outcomes[ 9 ]. Neurological markers and uterine complications did not show significant associations (p > 0.05). Interestingly, massive transfusion, although more prevalent, showed a lower risk (RR: 1.99), supporting that these are effectively managed with critical interventions. Rather than as a mortality predictor [ 40 ]. A significant increase in mortality was linked to higher MSS scores; all women with an MSS ≥ 7 died, whereas no deaths occurred below a threshold of five. The statistically significant correlation between MSS and mortality (r = 0.383) was in line with earlier studies from Brazil and Asia that demonstrated that higher MSS consistently predicted negative outcomes [ 41 ]. In the present study, Model II showed an enhanced ability to predict maternal mortality compared with Model I and the Maternal Severity Score. Model II, which is a multivariate logistic regression that integrates the maternal severity score along with other clinical factors, such as the timing of severity onset and the existence of cardiovascular or respiratory dysfunction, demonstrated excellent model alignment (Hoser–Lemeshow p = 0. 361) and an AUROC of 0. 939 (95% CI: 0. 911–0. 966 [ 32 ] [ 42 ]. The Maternal Severity Score's AUROC of 0.875, with acceptable discriminative power, enhances its use in triage and early warning systems. The performance of the maternal severity score (AUROC: 0.875), even without specific predictive power for subpopulations, can be used as a triage instrument, particularly in low-resource settings. Model II's superior Nagelkerke R² value (0.789) and good discriminatory performance indicate that incorporating severity indicators along with clinical and contextual factors significantly improves prediction accuracy. This highlights the global applicability of the WHO maternal near miss framework. The implementation of such models can guide focused critical interventions, case-mix adjustments, and monitoring systems to enhance maternal outcomes[ 9 ]. Strengths and Limitations This study has several merits. First, its prospective design minimizes recall and selection bias, enabling real-time surveillance of severe maternal outcomes using WHO’s near-miss criteria. Second, the integration and comparison of two predictive models—Maternal Severity Score (MSS) and Maternal Severity Index (MSI) -enhance the robustness of risk stratification and insights into prognostic modelling in maternal care. The use of detailed clinical parameters and a logistic regression model allows for more reliable evidence of maternal death predictors, enhancing its analytical rigor. Limitations are acknowledged. As a single-center study conducted at a tertiary referral hospital, its results limit its generalizability to primary or secondary healthcare settings or with different case mixes and resource levels. Moreover, the measurement of severity markers depends on the availability of diagnostic tests and laboratory support. Finally, although the WHO near-miss tool was effectively applied, variability across observers in the interpretation of clinical markers may influence outcomes. Conclusion The WHO maternal near-miss criteria proved high sensitivity and specificity in recognizing life-threatening complications of pregnancy, fortifying their value in surveillance of maternal health. The combined application of the Maternal Severity Score and Maternal Severity Index enhances the prediction of maternal death and provides a scalable method for the prognosis of severe maternal conditions. These findings emphasize the importance of integrating severity-based parameters into routine monitoring systems to strengthen the quality of maternal care, guide the distribution of resources, and eventually reduce preventable maternal deaths in low-resource settings. Declarations Authors' contributions BR (Bharathi Ramu) conceptualized and designed the study, collected data, drafted the manuscript, APH (Anil P. Hogade) contributed to the methodology and critically reviewed the analysis, added inputs and revised the manuscript. (Hema S Patil) monitored study methodology, supervised obstetric data interpretation and validated the clinical outcomes, and revised the manuscript. SCH (Sanjeev Chougale) assisted in data management and literature review. All authors read and approved the final manuscript. Acknowledgements: The authors are grateful to the faculty and staff of KAHER’s Jawaharlal Nehru Medical College (JNMC), Belagavi, for their support and cooperation throughout the study. We also extend our sincere thanks to the KAHER’s Dr. Prabhakar Kore Hospital and Medical Research Centre, Department of Obstetrics and Gynecology, for facilitating data access and coordination. The study received ethical clearance from the Institutional Ethics Committee, JNMC, KAHER, Belagavi. Funding: No funding was received for this study Ethics approval The authors confirm that all procedures involving human participants were conducted according to the ethical standards of the relevant clinical research ethics committee and in line with the principles outlined in the Declaration of Helsinki. Ethical clearance was obtained from the JNMC Institutional Ethics Committee (KAHER/EC/23-24/349-1) and registered with CTRI under Registration number: CTRI/2024/03/063871approved on Feb 1, 2024, confirmed registration on March 8, 2024. Consent to participate Written informed consent was obtained from all participants before data collection. The authors further affirm compliance with institutional policies regarding the confidentiality and publication of patient-related data. Consent for publication Ethical approval and institutional permission were obtained for this study. All participants were informed both verbally and in writing, and written informed consent was obtained before data collection. Additionally, written informed consent was obtained for the publication of anonymised data. Competing interests: None declared References Chhabra P. Maternal Near Miss: An Indicator for Maternal Health and Maternal Care. Indian J Community Med Off Publ Indian Assoc Prev Soc Med. 2014;39:132–7. World Health Organization. – 2011 - Evaluating the quality of care for severe pregnanc.pdf. Souza JP, Gülmezoglu AM, Carroli G, Lumbiganon P, Qureshi Z. The world health organization multicountry survey on maternal and newborn health: study protocol. BMC Health Serv Res. 2011;11:286. Firoz T, Trigo Romero CL, Leung C, Souza JP, Tunçalp Ö. Global and regional estimates of maternal near miss: a systematic review, meta-analysis and experiences with application. BMJ Glob Health. 2022;7:e007077. Mantel GD, Buchmann E, Rees H, Pattinson RC. Severe acute maternal morbidity: a pilot study of a definition for a near-miss. Br J Obstet Gynaecol. 1998;105:985–90. Silva FX, Katz L, Cecatti JG. Prognostic scores for prediction of maternal near miss and maternal death after admission to an intensive care unit: A narrative review. Health Care Women Int. 2023;44:1558–72. Vincent J-L, Moreno R. Clinical review: Scoring systems in the critically ill. Crit Care. 2010;14:207. Geller SE, Rosenberg D, Cox S, Brown M, Simonson L, Kilpatrick S. A scoring system identified near-miss maternal morbidity during pregnancy. J Clin Epidemiol. 2004;57:716–20. Pandit R, Jain V, Bagga R, Sikka P, Jain K. Applicability of WHO Maternal Severity Score (MSS) and Maternal Severity Index (MSI) Model to predict the maternal outcome in near miss obstetric patients: a prospective observational study. Arch Gynecol Obstet. 2019;300:49–57. Keepanasseril A, Balachandran DM, Sharma J, Maurya DK, Kar SS. External validation of the Maternal Severity Index for predicting maternal death following potentially life-threatening complications during pregnancy and childbirth: a single-centre, prospective observational study. BMJ Open. 2022;12:e067112. Kuruvilla S, Bustreo F, Kuo T, Mishra C, Taylor K, Fogstad H, et al. The Global strategy for women’s, children’s and adolescents’ health (2016–2030): a roadmap based on evidence and country experience. Bull World Health Organ. 2016;94:398–400. Miranda J, Miller S, Alfieri N, Lalonde A, Ivan-Ortiz E, Hanson C, et al. Global health systems strengthening: FIGO’s strategic view on reducing maternal and newborn mortality worldwide. Int J Gynecol Obstet. 2024;165:849–59. Cresswell –. 2023 - Trends in Maternal Mortality 2000 to 2020 Estimat.pdf. Goli S, Jaleel ACP. What is the cause of the decline in maternal mortality in India? Evidence from time series and cross-sectional analyses. J Biosoc Sci. 2014;46:351–65. Parmar NT, Parmar AG, Mazumdar VS. Incidence of Maternal Near-Miss Events in a Tertiary Care Hospital of Central Gujarat, India. J Obstet Gynaecol India. 2016;66(Suppl 1):315–20. Joshi T. Incidence of maternal near miss and mortality cases in central India tertiary care centre and evaluation of various causes. 2018. https://doi.org/10.21276/OBGYN.2018.4.2.4 Near-Miss. Obstetric Events and Maternal Deaths in a Rural Tertiary Care Center in North India | Cureus. https://www.cureus.com/articles/43334-near-miss-obstetric-events-and-maternal-deaths-in-a-rural-tertiary-care-center-in-north-india#!/ . Accessed 3 May 2025. Verma A, Choudhary R, Chaudhary R, Kashyap M. Maternal Near-Miss and Maternal Mortality in a Tertiary Care Center of Western Uttar Pradesh: A Retrospective Study. Cureus 15:e42697. Maternal near-miss. reviews: lessons from a pilot programme in India - PubMed. https://pubmed.ncbi.nlm.nih.gov/25236643/ . Accessed 3 May 2025. Nelissen EJ, Mduma E, Ersdal HL, Evjen-Olsen B, van Roosmalen JJ, Stekelenburg J. Maternal near miss and mortality in a rural referral hospital in northern Tanzania: a cross-sectional study. BMC Pregnancy Childbirth. 2013;13:141. Heitkamp A, Meulenbroek A, van Roosmalen J, Gebhardt S, Vollmer L, de Vries JI, et al. Maternal mortality: near-miss events in middle-income countries, a systematic review. Bull World Health Organ. 2021;99:693–F707. Maternal_Near_Miss_Operational_Guidelines.pdf. Tahmina S, Daniel M, Gunasegaran P. Emergency Peripartum Hysterectomy: A 14-Year Experience at a Tertiary Care Centre in India. J Clin Diagn Res JCDR. 2017;11:QC08–11. Souza JP, Gülmezoglu AM, Vogel J, Carroli G, Lumbiganon P, Qureshi Z, et al. Moving beyond essential interventions for reduction of maternal mortality (the WHO Multicountry Survey on Maternal and Newborn Health): a cross-sectional study. Lancet. 2013;381:1747–55. Maternal Near Miss. and quality of care in a rural Rwandan hospital | BMC Pregnancy and Childbirth | Full Text. https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-016-1119-1 . Accessed 3 May 2025. Purandare C, Bhardwaj A, Malhotra M, Bhushan H, Chhabra S, Shivkumar P. Maternal near-miss reviews: lessons from a pilot programme in India. BJOG Int J Obstet Gynaecol. 2014;121:105–11. Abha S, Chandrashekhar S, Sonal D. Maternal Near Miss: A Valuable Contribution in Maternal Care. J Obstet Gynaecol India. 2016;66(Suppl 1):217–22. Rathod AD, Chavan RP, Bhagat V, Pajai S, Padmawar A, Thool P. Analysis of near-miss and maternal mortality at tertiary referral centre of rural India. J Obstet Gynaecol India. 2016;66(Suppl 1):295–300. Verschueren KJ, Kodan LR, Paidin RR, Samijadi SM, Paidin RR, Rijken MJ et al. Applicability of the WHO maternal near-miss tool: A nationwide surveillance study in Suriname. J Glob Health. 10:020429. Kulkarni R, Kshirsagar H, Begum S, Patil A, Chauhan S. Maternal near miss events in India. Indian J Med Res. 2021;154:573–82. Goldenberg RL, Saleem S, Ali S, Moore JL, Lokangako A, Tshefu A, et al. Maternal near miss in low-resource areas. Int J Gynaecol Obstet Off Organ Int Fed Gynaecol Obstet. 2017;138:347–55. Souza JP, Cecatti JG, Haddad SM, Parpinelli MA, Costa ML, Katz L, et al. The WHO Maternal Near-Miss Approach and the Maternal Severity Index Model (MSI): Tools for Assessing the Management of Severe Maternal Morbidity. PLoS ONE. 2012;7:e44129. Witteveen T, de Koning I, Bezstarosti H, van den Akker T, van Roosmalen J, Bloemenkamp KW. Validating the WHO Maternal Near Miss Tool in a high-income country. Acta Obstet Gynecol Scand. 2016;95:106–11. Tura AK, Trang TL, van den Akker T, van Roosmalen J, Scherjon S, Zwart J, et al. Applicability of the WHO maternal near miss tool in sub-Saharan Africa: a systematic review. BMC Pregnancy Childbirth. 2019;19:79. Mustafa R, Hashmi H. Near-miss obstetrical events and maternal deaths. J Coll Physicians Surg–Pak JCPSP. 2009;19:781–5. Nelissen E, Mduma E, Broerse J, Ersdal H, Evjen-Olsen B, van Roosmalen J, et al. Applicability of the WHO maternal near miss criteria in a low-resource setting. PLoS ONE. 2013;8:e61248. Souza JP, Gülmezoglu AM, Carroli G, Lumbiganon P, Qureshi Z, WHOMCS Research Group. The world health organization multicountry survey on maternal and newborn health: study protocol. BMC Health Serv Res. 2011;11:286. Chimwaza Y, Hunt A, Oliveira-Ciabati L, Bonnett L, Abalos E, Cuesta C, et al. Early warning systems for identifying severe maternal outcomes: findings from the WHO global maternal sepsis study. eClinicalMedicine. 2025;79:102981. El Ayadi AM, Nathan HL, Seed PT, Butrick EA, Hezelgrave NL, Shennan AH, et al. Vital Sign Prediction of Adverse Maternal Outcomes in Women with Hypovolemic Shock: The Role of Shock Index. PLoS ONE. 2016;11:e0148729. Ehsan A, Zubair M, Islam A, Tariq A, Yaqub U, Shabbir N. Four Years Maternal Missed Mortality Ratio and Mortality Index at A Tertiary Care Hospital in Azad Kashmir. Pak Armed Forces Med J. 2024;74:946–50. Souza JP, Cecatti JG, Faundes A, Morais SS, Villar J, Carroli G, et al. Maternal near miss and maternal death in the World Health Organization’s 2005 global survey on maternal and perinatal health. Bull World Health Organ. 2010;88:113–9. Aoyama K, D’Souza R, Pinto R, Ray JG, Hill A, Scales DC, et al. Risk prediction models for maternal mortality: A systematic review and meta-analysis. PLoS ONE. 2018;13:e0208563. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6596205","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469824785,"identity":"575b2df9-ce36-4138-abb9-522e3ae0a30b","order_by":0,"name":"Bharathi 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16:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6596205/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6596205/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84682281,"identity":"87c21cb3-fd5f-4cab-92a0-1f75009ba56a","added_by":"auto","created_at":"2025-06-16 08:35:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33823,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationships between the maternal severity score and the maternal severity index\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6596205/v1/cdaf5699a0c3201cb935f5c8.png"},{"id":84682280,"identity":"675eee6e-fc3f-4fd4-8b1f-a15e57719d6b","added_by":"auto","created_at":"2025-06-16 08:35:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":13146,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curve for Model I\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6596205/v1/6a865baa2ca28fee2c7ea3e2.png"},{"id":84682286,"identity":"d4f2daed-d615-47ff-a107-5ad6c7784de8","added_by":"auto","created_at":"2025-06-16 08:35:36","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":156648,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curve for Model II\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6596205/v1/06856123d5ccbec82b0bbd5a.jpeg"},{"id":100237271,"identity":"dc9fbbd6-13d6-4996-884c-890de6adda51","added_by":"auto","created_at":"2026-01-14 12:41:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1571267,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6596205/v1/805fe9ea-de36-4bdb-bd96-6d0ada9ce770.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMaternal mortality prediction using WHO near miss criteria and Maternal severity models: Evidence from a Tertiary Care Study in India\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWomen's health during pregnancy, delivery, and the postpartum phase is the focus of maternal health, and a marker of the efficiency of the health care system of any country [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn any setting, women who develop severe acute morbidity during pregnancy share many pathological and circumstantial factors related to their condition. While some of these women die, a proportion of them narrowly escape death. Maternal death is defined as the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and the site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management, but not from accidental or incidental causes[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Nearly 1,000 women die every day, and ten million women present with complications related to pregnancy every year around the world.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eTo evaluate maternal health, women who survive life-threatening complications must be thoroughly examined. In this context, the WHO defines maternal near miss (MNM) as \"a woman who almost died but survived a complication that occurred during pregnancy, childbirth, or within 42 days of termination of pregnancy\" [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo reduce avoidable maternal mortality and enhance maternal health outcomes, addressing maternal near-miss morbidity is imperative. The World Health Organization (WHO) adopted maternal near-miss definitions and identification criteria in 2008. These criteria were developed via a comprehensive evaluation of earlier research. critical evaluations were conducted on intensive-care prognostic scoring systems, including the APACHE II score, SAPS, MODS, and especially the SOFA score as a possible indicators of clinical severity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhysiological changes during pregnancy affect some of the markers and may lead to overestimation or underestimation of severity depending on parameters, limiting the use of the scoring system in obstetric patients, and complications such as eclampsia, acute fatty liver during pregnancy, HELLP, and amniotic fluid embolism have distinct characteristics that may not be adequately addressed by the scoring system [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The WHO's near-miss approach is a standardised measure consisting of 25 severity markers of life-threatening conditions, and each severity marker is associated with a specific mortality risk. The maternal severity score (MSS) assigns a score based on the presence of specific severity markers (life-threatening conditions), and the maternal severity index (MSI) estimates the probability of death of women with pregnancy-related complications, particularly those with life-threatening conditions; both of these tools are very useful in assessing the quality of maternal health care [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobally, between 2000 and 2020, the global maternal mortality rate (MMR) declined by 34.3%, from 339 deaths to 223 deaths per 100,000 live births[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. As per the 2020 report by the United Nations Maternal Mortality Estimation Inter-Agency, India\u0026rsquo;s maternal mortality ratio (MMR) has experienced a notable reduction, decreasing from 384 per 100,000 live births in 2000 to 103 in 2020. In contrast, the global MMR experienced a decline to only 223 from 339 over the same timeframe. The annual reduction rate (ARR) for the global MMR between 2000 and 2020 was 2.07%, whereas India achieved a more significant decline, with an ARR of 6.36%, highlighting the advancements made in the maternal health initiative [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e The Government of India (GOI) guidelines mandate that maternal death reviews (MDRSs) be systematically carried out in all healthcare facilities. Nonetheless, reviews of maternal near miss (MNM) events offer additional benefits over MD reviews [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the last ten years, various studies on MNM [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e],[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e],[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], have been undertaken in India and other developing countries, [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e],[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], utilising the WHO criteria. The results emphasise the importance of MNM reviews in enhancing maternal health policies, improving the quality of healthcare delivery, and averting maternal deaths through timely interventions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].Hence, this study was conducted to assess the WHO MNM criteria and Maternal severity markers as predictors of maternal mortality\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e: A prospective observational study was conducted at KAHER\u0026rsquo;s Dr. Prabhakar Kore\u0026nbsp;Hospital and Medical Research Centre, Belagavi, India,\u0026nbsp;for 1 year (1 February\u0026nbsp;2024\u0026ndash;31\u0026nbsp;January 2025)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Setting\u003c/strong\u003e: A hospital offering 2000+ beds and state-of-the-art infrastructure, including 270 ICU beds and a wide range of specialities.\u003c/p\u003e\n\u003cp\u003eAnnually, 3500 to 4000 deliveries are conducted at the facility. The hospital acts as a provincial referral hospital for high-risk obstetric cases from health centres and other neighboring states of Goa and Maharashtra.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Consideration\u003c/strong\u003e: Ethical clearance was obtained from the JNMC Institutional Ethics Committee KAHER/EC/23-24/349-1) (CTRI Registration number: CTRI/2024/03/063871) approved on Feb 1, 2024, confirmed registration on March 8, 2024. Informed consent was obtained from pregnant women who were admitted and included in the study. The WHO near-miss tool was used for data collection. The confidentiality of the data was maintained\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility criteria:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003epopulation\u003c/strong\u003e: Pregnant women fulfilling the WHO maternal near miss criteria (2011) and having maternal deaths as defined by the WHO were included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe inclusion criteria\u003c/strong\u003e were the presence of any of the conditions listed in the WHO maternal near miss criteria (i.e., potentially life-threatening conditions, WHO life-threatening conditions) and maternal death.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe exclusion criteria were as follows\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Women who developed complications more than 42 days after the termination of pregnancy were not eligible.\u003c/p\u003e\n\u003cp\u003eThis study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOperational definitions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaternal near-miss\u003c/strong\u003e (MNM) refers to a woman who nearly died but survived a complication that occurred during pregnancy, childbirth or within 42 days of termination of pregnancy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaternal death\u003c/strong\u003e (MD) is the death of a woman while pregnant or within 42 days of termination of pregnancy or its management, but not from accidental or incidental causes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSevere maternal outcome ratio (SMOR)\u003c/strong\u003e refers to the number of women with life-threatening conditions (MNM + MD) per 1000 live births (LB). This indicator gives an estimate of the amount of care and resources that would be needed in an area or facility [SMOR = (MNM + MD)/LB].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMNM ratio (MNMR)\u003c/strong\u003e refers to the number of maternal near-miss cases per 1000 live births (MNMR = MNM/LB), This indicator gives an estimation of the amount of care and resources that would be needed in an area or facility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaternal near-miss mortality ratio (MNM: 1 MD)\u003c/strong\u003e refers to the ratio between maternal near-miss cases and maternal deaths. Higher ratios indicate better care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMortality index\u003c/strong\u003e refers to the number of maternal deaths divided by the number of women with life-threatening conditions expressed as a percentage [MI = MD/ (MNM + MD)]. The higher the index the more women with life-threatening conditions die (low quality of care), whereas the lower the index the fewer women with life-threatening conditions die (better quality of care).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the statistical analyses were performed via IBM SPSS version 24, R software version 4.3.3, and Excel. The performance of the WHO criteria for case identification was assessed in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), and negative likelihood ratio (NLR). Sensitivity and specificity were calculated to determine the accuracy of maternal death (MD) and near-miss identification. \u0026nbsp;Likelihood ratios were used to assess the strength of case identification, and predictive values were used to quantify the probability of a correct classification.\u003c/p\u003e\n\u003cp\u003eThe associations between maternal severity markers and mortality risk were analyzed via relative risk (RR) with 95% confidence intervals (CIs) for clinical indicators. The chi-square test was employed to determine the statistical significance of associations between categorical variables, with the significance level set at p\u0026lt;0.05. Descriptive statistics, including the mean and standard deviation, were used to summarize the maternal severity score and maternal severity index. A two-tailed significance test was conducted to determine whether the correlation was statistically significant, with the significance level set at p\u0026lt;0.01. The association between the maternal severity score and maternal mortality was analyzed via Pearson\u0026rsquo;s correlation coefficient to determine the strength and direction of the relationship. A significance test was conducted at the 0.01 level to assess the statistical significance of the correlation.\u003c/p\u003e\n\u003cp\u003eTo compare the performance of Models I and II and the maternal severity score in predicting maternal death, binary logistic regression was applied. The Hosmer\u0026ndash;Lemeshow test was used to assess the goodness of fit, whereas the Nagelkerke R\u0026sup2; test was employed to evaluate the explanatory power of the models. The percentage of maternal deaths with a model-estimated death probability greater than 50% was analyzed for different subpopulations. Finally, the area under the operating characteristic curve (AUROC) was calculated to measure the models\u0026apos; discrimination ability in predicting maternal deaths. The statistical analyses were validated against existing evidence to determine the robustness and reliability of the findings in maternal health research.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 4068 women were admitted to the facility during the one-year data collection period, and live born babies were 3978. The study population comprised 310 women meeting the inclusion criteria. Among this population, 295 women met any of the WHO criteria, women presenting with at least one of the severity markers classified as WHO life-threatening conditions included 15 maternal deaths and 191 maternal near misses, representing a severe maternal outcome ratio of 51.7 per 1000 live births. The MNM:MD ratio was 12.73:1, and the MNM ratio was 47.9 per 1000 live births, with a mortality index of 7.28%. These findings reflect the burden of severe morbidity in the study setting.\u003c/p\u003e\n\u003cp\u003e(Table 1) shows the diagnostic performance of the WHO near-miss criteria in two populations: women with overall WHO-defined conditions and those with WHO-defined organ dysfunction. The criteria achieved a sensitivity of 100% (95% CI: 78.20\u0026ndash;100.00) and a specificity of 93.11% (95% CI: 92.31\u0026ndash;93.85). When the analysis focused on organ dysfunction markers, the diagnostic accuracy improved to 95.43% (95% CI: 94.75\u0026ndash;96.04), with a higher positive likelihood ratio and specificity, while maintaining optimal sensitivity and negative predictive value.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(Table 2) presents the associations of severity markers (WHO life-threatening conditions) with mortality and the prevalence of cases with severity markers per thousand deliveries, which range from 0.49-24.82. The specific mortality rate varied from 6.39% to 100%, and all the life-threatening conditions were heterogeneously associated with maternal deaths. 7 Life-threatening conditions present relative risks of 5 and 10, Gasping (RR: 12.63), Oxygen Saturation \u0026lt;90% for 60 minutes (RR: 8.1), Failure to Form Clots(RR: 9.56), Severe Hyperbilirubinemia (\u0026gt;6 mg/dL) (RR: 7.77), Severe Tachypnoea -RR \u0026gt;40/min (RR: 5.47) Severe Thrombocytopenia \u0026lt;50,000/mm\u0026sup3; (RR: 3.46), Severe Azotemia or Creatinine \u0026gt;3.5 mg/dL (RR: 5.18) these markers are significant indicators of deteriorating physiological conditions. Three markers (Use of Vasoactive Drugs (RR: 18.23), Oliguria unresponsive to fluids and diuretics (RR: 14.59), Severe Hypoxemia -PaO₂/FiO₂ \u0026lt; 200 (RR: 10.78) presented with relative risks ranging from 10 to 20, suggesting circulatory collapse and severe metabolic disorders, and 3 conditions i.e., Shock (RR: 35.72), Severe Hypoperfusion (Lactate \u0026gt;5 mmol/L or \u0026gt;45 mg/dL) ( RR: 43.09), Severe Acidosis (pH \u0026lt;7.0) (RR: 25.38) presented relative risks ranging from 20 to 60 and 2 life-threatening conditions with Relative risk \u0026gt;60.i.e CPR (RR: 155.65), Intubation \u0026amp; Ventilation (non-anaesthetic) (RR: 157.73) \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eShock (RR: 35.72; p \u0026lt; 0.001), vasoactive administration (RR: 40.74; p \u0026lt; 0.001), cardiac arrest (RR: 14.5; p \u0026lt; 0.001), and CPR (RR: 155.65; p \u0026lt; 0.001). severe hypoxemia (RR: 10.78; p \u0026lt; 0.001), oxygen desaturation (RR: 8.16; p \u0026lt; 0.001), and non-anesthesia intubation (RR: 157.73; p \u0026lt; 0.001) were also strong predictors. Metabolic dysfunction as severe hypoperfusion (RR: 43.09; p \u0026lt; 0.001), acidosis (RR: 25.38; p \u0026lt; 0.001), and renal dysfunction (e.g., oliguria: RR: 14.59; p \u0026lt; 0.001), showed a significant link to mortality rates. In contrast, neurological disorders (including prolonged unconsciousness, stroke, and status epilepticus) and uterine complications (e.g., infection-related hysterectomy) did not correlate with mortality (p \u0026gt; 0.05) [23]. Additionally, massive transfusion was linked to a lower risk (RR: 1.99; p = 0.009), suggests efficient management of complications. These findings indicate extremely high mortality risks and also identify severity markers as significant predictors of physiological deterioration and adverse outcomes.\u003c/p\u003e\n\u003cp\u003e(Table 3) shows that the maternal severity score has a mean of 1.56 with a standard deviation of 2.08, indicating that most women were managed before complications became life-threatening, suggesting that early intervention effectively reduced the risk of maternal mortality. The Maternal Severity Index has a higher mean of 3.77, with a much larger standard deviation of 12.95, showing its strong predictive value for identifying women at increased risk of maternal mortality.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe correlation between the Maternal Severity Score and the Maternal Severity Index is strong (R = 0.805) and statistically significant (p =\u0026nbsp;0.0001*), suggesting that higher scores on one measure are associated with higher scores on the other. This significant positive correlation indicates that both measures are closely related in assessing maternal health severity. (Figure 1) A scatter plot illustrates a positive correlation between the Maternal Severity Score and\u0026nbsp;the\u0026nbsp;Maternal Severity Index, with a clear upward trend. As the severity score increases, the severity index also tends to rise, confirming the significant relationship between the two variables. (Table 4) illustrates the\u0026nbsp;relationships\u0026nbsp;between maternal severity scores and mortality across different severity score categories. The sharp increase in mortality with higher severity scores suggests a strong association between the severity of a maternal condition and the likelihood of death. The Pearson correlation coefficient of 0.383 is significant at the 0.01 level. supports the use of the maternal severity score as a predictor\u0026nbsp;of\u0026nbsp;maternal mortality risk, suggesting that higher scores reflect more critical conditions that require immediate medical attention.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(Table 5) shows the performance of two binary logistic regression models (Model I and Model II) and the maternal severity score in predicting maternal death. Model I: This model, which is based solely on the maternal severity score, yielded an AUROC of 0.775 (95% CI: 0.725\u0026ndash;0.825) (Figure 2) reflecting Hosmer\u0026ndash;Lemeshow test produced a p-value of 0.012, showing a marginal fit, whereas the Nagelkerke R\u0026sup2; was 0.405, suggesting that approximately 40.5% of the variance in maternal mortality could be explained by this model.\u003c/p\u003e\n\u003cp\u003eModel II: The multivariate model that included additional clinical parameters performed markedly better, achieving an AUROC of 0.939 (95% CI: 0.911\u0026ndash;0.966) (Figure 3). The Hosmer\u0026ndash;Lemeshow test p value for Model II was 0.361, indicating a very good fit, and the Nagelkerke R\u0026sup2; value was 0.789, reflecting robust explanatory power for predicting maternal deaths. Furthermore, the percentage of maternal deaths with a model-estimated death probability greater than 50% was greater in Model II (60.3%\u0026ndash;62.8%) than in Model I (58.7%\u0026ndash;60.6%), reinforcing the clinical utility of including both the MSS and clinical parameters in the prediction of severe maternal outcome.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1 Diagnostic\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eaccuracy\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eWHO\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecriteria\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;with\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eseverity\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;markers (\u003c/strong\u003e\u003cstrong\u003elife-\u003c/strong\u003e\u003cstrong\u003ethreatening complications) in\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe prediction of\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;maternal\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003edeath\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 191px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll women\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Organ dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Maternal death\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Maternal death\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;WHO criterion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e3978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e3978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eAccuracy estimator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003eSensitivity (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 191px;\"\u003e\n \u003cp\u003e100 (78.20-100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 186px;\"\u003e\n \u003cp\u003e100 (78.20 - 100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003eSpecificity (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 191px;\"\u003e\n \u003cp\u003e93.11 (92.31- 93.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 186px;\"\u003e\n \u003cp\u003e95.43 (94.75 - 96.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003ePositive Likelihood Ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 191px;\"\u003e\n \u003cp\u003e14.52 (13.00 -16.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 186px;\"\u003e\n \u003cp\u003e21.87 (19.04 - 25.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNegative Likelihood Ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 191px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 186px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003ePositive Predictive Value (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 191px;\"\u003e\n \u003cp\u003e53.7 (50.96- 56.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 186px;\"\u003e\n \u003cp\u003e52.39 (48.93 - 55.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNegative Predictive Value (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 191px;\"\u003e\n \u003cp\u003e100 (99.91- 100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 186px;\"\u003e\n \u003cp\u003e100 (99.91- 100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Association\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of severity markers (WHO-life-threatening conditions) with maternal deaths\u003c/strong\u003e.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMD (Maternal death)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases presenting the severity marker per 1000 deliveries*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative Risk(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiovascular dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 286px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eShock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e14.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e24.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e35\u003c/strong\u003e.72 (4.57 - 279.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eUse Of Vasoactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e40.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e18.23 (5.2 - 63.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eCardiac arrest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e14.5 (0.69 - 306.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eCPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e82.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e155.65 (15.18 - 1595.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eSevere hypoperfusion Lactate \u0026gt;5 mmol or \u0026gt;45 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e48.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e43.09 (8.83 - 210.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003esevere acidosis PH \u0026lt;7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e30.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e25.38 (5.44 - 118.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRespiratory dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eGasping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e12.63 (1.22 - 130.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eSevere tachypnoea (RR \u0026gt;40 bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e33.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e5.47 (1.22 - 24.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eSevere hypoxemia (PA02/FiO2 \u0026lt;200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e10.78 (2.89 - 40.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eO2 Saturation \u0026lt;90% for 60 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e28.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e8.16 (2.66 - 25.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eIntubation \u0026amp; Ventilation not related to anesthesia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e45.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e157.73 (9.04 - 2751.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRenal dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eOliguria Nonresponsive to fluids and Diuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e66.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e14.59 (3.13 - 67.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eDialysis for acute renal failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e6.18 (0.95 - 40.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eSevere acute azotemia (creatinine \u0026gt;3.5 mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e33.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e5.18 (0.44 - 60.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoagulation/hematological dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eFailure to form clots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e36.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e9.56 (3.02 - 30.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eMassive blood transfusion \u0026gt; 5 units\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e15.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e11.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.99 (0.69 - 5.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eSevere acute thrombocytopenia (\u0026lt;50.000 platelets)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e19.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.46 (1.08 - 11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHepatic dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eSevere acute hyperbilirubinemia \u0026gt;6.0 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e7.77 (1.01 - 59.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeurological dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eProlonged conscious/coma\u0026gt;12 hrs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.22 (0.06 - 23.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.72 (0.12 - 63.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eStatus epilepticus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.62 (0.03 - 11.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUterine dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003ehemorrhage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e24.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.91 (0.32 - 2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eInfection Leading to Hysterectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.93 (0.09 - 40.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eHysterectomy (prevention of PPH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.69 (0.04 - 12.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"bottom\" style=\"width: 625px;\"\u003e\n \u003cp\u003e\u0026apos;N=3987 (Number of deliveries) p \u0026lt; 0.05*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Correlation between maternal severity scores and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ematernal severity index\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eaccording to the\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Karl\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePearson\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;correlation coefficient\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eStd. Dv.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003er (X, Y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003et value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eMaternal severity scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eMaternal severity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e12.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.8052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e23.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Association of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe maternal severity score\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;with maternal\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003edeath\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"664\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eMaternal severity score (Counts)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eMortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.00-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.00-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0569\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.01-2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.2327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.02-6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.04-10.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.7898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e9.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e3.29-27.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.13-28.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.6164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e25.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e14.69-44.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e25.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e14.69-44.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e23.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e13.91-40.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e22.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e13.22-36.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e22.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e13.22-36.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e23.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e13.91-40.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 571px;\"\u003e\n \u003cp\u003eThe Pearson correlation coefficient between the maternal severity score and mortality is 0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"688\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 50.0294%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 5 Performance Comparison of Models I and II and Maternal Severity Score in Predicting Maternal Death\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.5344%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1065%;\"\u003e\n \u003cp\u003eHosmer‒Lemeshow test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1065%;\"\u003e\n \u003cp\u003eNagelkerke R\u003csup\u003e2\u003c/sup\u003e test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.1966%;\"\u003e\n \u003cp\u003ePercentage of maternal deaths with a model-estimated death probability \u0026gt;50% (subpopulation \u0026quot;A\u0026quot;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8587%;\"\u003e\n \u003cp\u003ePercentage of maternal deaths with a model-estimated death probability \u0026gt;50% (subpopulation \u0026quot;B\u0026quot;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.153%;\"\u003e\n \u003cp\u003eAUROC with 95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.5344%;\"\u003e\n \u003cp\u003eModel I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1065%;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1065%;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.1966%;\"\u003e\n \u003cp\u003e58.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8587%;\"\u003e\n \u003cp\u003e60.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.153%;\"\u003e\n \u003cp\u003e0.775 (0.725 \u0026ndash; 0.825)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.5344%;\"\u003e\n \u003cp\u003eModel II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1065%;\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1065%;\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.1966%;\"\u003e\n \u003cp\u003e60.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8587%;\"\u003e\n \u003cp\u003e62.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.153%;\"\u003e\n \u003cp\u003e0.939 (0.911 \u0026ndash; 0.966)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.5344%;\"\u003e\n \u003cp\u003eMaternal Severity Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1065%;\"\u003e\n \u003cp\u003eN.A.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1065%;\"\u003e\n \u003cp\u003eN.A.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.1966%;\"\u003e\n \u003cp\u003eN.A.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8587%;\"\u003e\n \u003cp\u003eN.A.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.153%;\"\u003e\n \u003cp\u003e0.875 (0.816 \u0026ndash; 0.934)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study assessed the effectiveness of the WHO maternal near miss (MNM) criteria and maternal severity markers that predict maternal mortality in a tertiary care setting. These findings show that the WHO criteria can reliably detect maternal deaths because of their high sensitivity and diagnostic accuracy and strong association of Severity markers with maternal mortality.\u003c/p\u003e \u003cp\u003eIn our study, the Severe Maternal Outcome Ratio (SMOR) of 51.7 per 1000 live births, indicating an increased burden of life-threatening maternal complications. These Observations align with existing findings from India and other low- and middle-income countries. To illustrate, the WHO multicountry Survey on Maternal and Newborn Health reported an SMOR of 40.3 per 1000 live births across facilities in Africa, Asia, Latin America, and the Middle East, including India [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, single-country studies showed higher SMOR: Rwanda [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Tanzania [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], with SMOR of 81.1, 101.1, respectively. Purandare et al.'s multicenter study revealed 92.3 per 1000 live births [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], whereas Roopa et al.'s 76.2 in Karnataka [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], Maharashtra's 131.5 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], These high SMOR values, especially in referral centers, emphasize the cumulative effects of delayed presentation, inadequate antenatal surveillance, and the referral of high-risk cases at a critical stage of complications.\u003c/p\u003e \u003cp\u003eOur study showed a Mortality Index (MI) of 7.27%, which is lower compared with earlier studies, despite the increased SMOR. In particular, 13.1% MI was reported by Souza et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]and 12% by Verschueren et al [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], 17.7% MI showed by Nelissen et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and 11.2% MI was reported by Purandare et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and we observed MNM:MD ratio 12.7:1 which is in line with Kulkarni et al [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and lower compared to Goldenberg et al which is 26:1 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Even when a high SMOR is present, a relatively lower MI suggests that even though many women suffered from severe morbidity, the healthcare system was able to prevent a significant number of maternal deaths by ensuring that they had access to timely interventions and critical care.\u003c/p\u003e \u003cp\u003eFurthermore, the Maternal Near Miss Ratios (MNMR), 47.9 per 1000 live births, highlight the need to improve health system responsiveness. These findings align with findings from various Indian and international studies. For example, 16.6/1000 live births by Verma et al. were conducted in Uttar Pradesh, India, which is lower than our study. Purandare et al. noted an MNMR of 88.3 per 1000 live births in a multicentric Indian study [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], Roopa et al. recorded 80.2 per 1000 live births in Karnataka [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e],Rathod et al. documented 121.4 per 1000 live births in Maharashtra [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], These high MNMR highlight the major challenge presented by life-threatening maternal conditions treated at tertiary centers.\u003c/p\u003e \u003cp\u003eThe findings of our study regarding diagnostic accuracy align closely with those reported by Souza et al. (2012), which analyzed data from 29 countries in the WHO Global Survey on Maternal and Perinatal Health. Their multicentric study achieved a negative predictive value (NPV) of 99.9% and nearly 100% sensitivity, demonstrating that the WHO criteria effectively identify all maternal deaths and near-miss events without missing serious complications [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].T. Witteveen et al.study [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] showed opposite findings that organ dysfunction-based criteria of the WHO MNM tool fail to identify nearly two-thirds of SAMM cases and more than one-third of maternal deaths [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].Tura et al. studied sub-Saharan Africa and verified the WHO MNM criteria, finding specificity range of 85–96% and a sensitivity of 100%. This aligns with our findings, especially regarding specificity in our resource setting [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Our study's modest PPV (~ 53%) is consistent with the varying PPV values across situations reported by Souza et al. and Tura et al. This difference may be explained by the case mix, resource availability, and the application or interpretation of specific management-based criteria, which may exaggerate severe morbidity in institutions with better access to advanced care [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA significantly greater relative risk (RR) for maternal death was shown by several severity markers, particularly those linked to metabolic, respiratory, and cardiovascular failure. Conditions such as cardiac arrest, the need for cardiopulmonary resuscitation, shock, and severe lactic acidosis have RRs over 40, indicating rapid systemic decompensation and critical illness. These results are consistent with those of multicountry WHO research, which revealed a consistent association between increased mortality risk and shock and organ failure [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Cardiovascular severity markers such as shock, use of vasoactive drugs, cardiac arrest, and CPR demonstrated strong associations with mortality (p \u0026lt; 0.001), in line with findings from WHO’s Multicounty Survey, circulatory dysfunction as a key predictor of maternal mortality. Severe hypoxemia and non-anaesthesia intubation, as a key contributor and predictor of maternal death, comply with previous validation studies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSevere hypoperfusion, acidosis, and oliguria are notably associated with poor outcomes[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Neurological markers and uterine complications did not show significant associations (p \u0026gt; 0.05). Interestingly, massive transfusion, although more prevalent, showed a lower risk (RR: 1.99), supporting that these are effectively managed with critical interventions. Rather than as a mortality predictor [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA significant increase in mortality was linked to higher MSS scores; all women with an MSS ≥ 7 died, whereas no deaths occurred below a threshold of five. The statistically significant correlation between MSS and mortality (r = 0.383) was in line with earlier studies from Brazil and Asia that demonstrated that higher MSS consistently predicted negative outcomes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, Model II showed an enhanced ability to predict maternal mortality compared with Model I and the Maternal Severity Score. Model II, which is a multivariate logistic regression that integrates the maternal severity score along with other clinical factors, such as the timing of severity onset and the existence of cardiovascular or respiratory dysfunction, demonstrated excellent model alignment (Hoser–Lemeshow p = 0. 361) and an AUROC of 0. 939 (95% CI: 0. 911–0. 966 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Maternal Severity Score's AUROC of 0.875, with acceptable discriminative power, enhances its use in triage and early warning systems. The performance of the maternal severity score (AUROC: 0.875), even without specific predictive power for subpopulations, can be used as a triage instrument, particularly in low-resource settings. Model II's superior Nagelkerke R² value (0.789) and good discriminatory performance indicate that incorporating severity indicators along with clinical and contextual factors significantly improves prediction accuracy. This highlights the global applicability of the WHO maternal near miss framework. The implementation of such models can guide focused critical interventions, case-mix adjustments, and monitoring systems to enhance maternal outcomes[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThis study has several merits. First, its prospective design minimizes recall and selection bias, enabling real-time surveillance of severe maternal outcomes using WHO’s near-miss criteria. Second, the integration and comparison of two predictive models—Maternal Severity Score (MSS) and Maternal Severity Index (MSI) -enhance the robustness of risk stratification and insights into prognostic modelling in maternal care. The use of detailed clinical parameters and a logistic regression model allows for more reliable evidence of maternal death predictors, enhancing its analytical rigor.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eLimitations are acknowledged. As a single-center study conducted at a tertiary referral hospital, its results limit its generalizability to primary or secondary healthcare settings or with different case mixes and resource levels. Moreover, the measurement of severity markers depends on the availability of diagnostic tests and laboratory support. Finally, although the WHO near-miss tool was effectively applied, variability across observers in the interpretation of clinical markers may influence outcomes.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe WHO maternal near-miss criteria proved high sensitivity and specificity in recognizing life-threatening complications of pregnancy, fortifying their value in surveillance of maternal health. The combined application of the Maternal Severity Score and Maternal Severity Index enhances the prediction of maternal death and provides a scalable method for the prognosis of severe maternal conditions. These findings emphasize the importance of integrating severity-based parameters into routine monitoring systems to strengthen the quality of maternal care, guide the distribution of resources, and eventually reduce preventable maternal deaths in low-resource settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBR (Bharathi Ramu) conceptualized and designed the study, collected data, drafted the manuscript, APH (Anil P. Hogade) contributed to the methodology and critically reviewed the analysis, added inputs and revised the manuscript. (Hema S Patil) monitored study methodology, supervised obstetric data interpretation and validated the clinical outcomes, and revised the manuscript. SCH (Sanjeev Chougale) assisted in data management and literature review.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;All authors read and approved the final manuscript.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors are grateful to the faculty and staff of KAHER\u0026rsquo;s Jawaharlal Nehru Medical College (JNMC), Belagavi, for their support and cooperation throughout the study. We also extend our sincere thanks to the KAHER\u0026rsquo;s Dr. Prabhakar Kore Hospital and Medical Research Centre,\u0026nbsp;Department of Obstetrics and Gynecology, for facilitating data access and coordination. The study received ethical clearance from the Institutional Ethics Committee, JNMC, KAHER, Belagavi.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding: No funding was received for this study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that all procedures involving human participants were conducted according to the ethical standards of the relevant clinical research ethics committee and in line with the principles outlined in the Declaration of Helsinki. Ethical clearance was obtained from the JNMC Institutional Ethics Committee (KAHER/EC/23-24/349-1) and registered with CTRI under Registration number: CTRI/2024/03/063871approved on Feb 1, 2024, confirmed registration on March 8, 2024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants before data collection. The authors further affirm compliance with institutional policies regarding the confidentiality and publication of patient-related data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval and institutional permission were obtained for this study. All participants were informed both verbally and in writing, and written informed consent was obtained before data collection. Additionally, written informed consent was obtained for the publication of anonymised data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests: None declared\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChhabra P. Maternal Near Miss: An Indicator for Maternal Health and Maternal Care. Indian J Community Med Off Publ Indian Assoc Prev Soc Med. 2014;39:132\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. \u0026ndash;\u0026thinsp;2011 - Evaluating the quality of care for severe pregnanc.pdf.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouza JP, G\u0026uuml;lmezoglu AM, Carroli G, Lumbiganon P, Qureshi Z. The world health organization multicountry survey on maternal and newborn health: study protocol. 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PLoS ONE. 2018;13:e0208563.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Maternal near miss, Maternal severity score, Maternal severity index. Mortality Index, Prediction, Quality of care.","lastPublishedDoi":"10.21203/rs.3.rs-6596205/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6596205/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMaternal mortality remains a critical public health issue, particularly in low- and middle-income countries (LMICS). Over the past two decades, there has been a significant global reduction in maternal mortality. As maternal deaths continue to decline, measuring maternal morbidity\u0026mdash;including the near-miss ratio, mortality index, severe maternal outcome ratio, and maternal near-miss mortality ratio\u0026mdash;is essential for assessing the quality of obstetric care. This study was conducted to validate the performance of the WHO MNM criteria and compare predictive models that integrate the Maternal Severity Score and Maternal Severity Index to predict maternal mortality in a tertiary care setting.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA prospective observational study was conducted at KAHER\u0026rsquo;s Dr. Prabhakar Kore Hospital and Medical Research Centre, Belagavi, India (1st February 2024\u0026ndash;31st January 2025). Pregnant women fulfilling the WHO maternal near-miss criteria were enrolled, and diagnostic accuracy tests for overall WHO criteria and organ dysfunction severity markers were performed. Pearson\u0026rsquo;s correlation coefficient was used to determine the association between MSS and MSI. Two binary logistic regression models to predict the probability of maternal death were developed and compared via the area under the receiver operating curve (AUROC), with additional assessment via Nagelkerke R\u0026sup2; and the Hosmer\u0026ndash;Lemeshow goodness-of-fit test.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOut of the 295 women identified with the WHO maternal near miss criteria, 191 fulfilled the criteria for maternal near misses, and 15 resulted in maternal death. The severe maternal outcome ratio (SMOR) was 51.7, the MNM ratio was 47.9 per 1,000 live births, and the mortality index was 7.27%. indicating that a majority of women with life-threatening conditions survived with better quality of care. The diagnostic accuracy of the WHO near-miss criteria showed good sensitivity (100%) and high specificity (93.11%) and improved significantly, up to 95.43%, when the organ dysfunction subset was used. The number of cases with severity markers per thousand deliveries ranged from 0.49\u0026ndash;24.82. The correlation between the Maternal Severity Score and the Maternal Severity Index was strong (R\u0026thinsp;=\u0026thinsp;0.805, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). supporting the internal validity of severity assessment tools, the multivariate logistic regression model that included additional clinical parameters performed better, achieving an AUROC of 0.939, indicating excellent discriminatory ability for predicting maternal mortality and underscoring the clinical utility of the enhanced model.\u003c/p\u003e","manuscriptTitle":"Maternal mortality prediction using WHO near miss criteria and Maternal severity models: Evidence from a Tertiary Care Study in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 08:35:31","doi":"10.21203/rs.3.rs-6596205/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8d02e84a-72bc-485c-b2ba-8be262b51222","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-14T12:40:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 08:35:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6596205","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6596205","identity":"rs-6596205","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}